Principal Engineer - AI System Design

Requirements :

- 14+ years of experience in software engineering, AI systems design, or cybersecurity solution architecture.

- Strong programming expertise in Python and GraphQL API development.

- Proven experience building microservices and REST APIs, integrated with modern CI/CD pipelines and Kubernetes for scalability.

- Deep understanding of the AWS Cloud Platform, including Bedrock, SageMaker, Lambda, ECS, and other core services.

- Hands-on expertise with LLMs (GPT, Claude, and Llama) and frameworks such as LangChain, Transformers, and Bedrock SDKs.

- Experience designing RAG pipelines, agentic AI workflows, and AI-driven automation for enterprise environments.

- Skilled in working with PostgreSQL, MongoDB, Elasticsearch/OpenSearch, and vector databases like Faiss and Pinecone.

- Strong foundation in observability, monitoring, secure architecture, and team leadership for production-grade AI deployments.

Ideal Candidate Profile :

- The ideal candidate brings 14+ years of experience in software engineering, AI systems design, or cybersecurity solution architecture, with strong programming expertise in Python and GraphQL API development.

- They have proven experience building scalable microservices and REST APIs, integrated with modern CI/CD pipelines and Kubernetes-based environments.

- The role requires deep hands-on knowledge of the AWS Cloud platform, including services such as Bedrock, SageMaker, Lambda, ECS, EKS, S3, IAM, CloudWatch, and API Gateway.

- The candidate should demonstrate practical expertise with large language models including GPT, Claude, and Llama, along with frameworks and tools such as LangChain, Hugging Face Transformers, Bedrock SDKs, and prompt engineering techniques.

- Experience designing and implementing RAG pipelines, agentic AI workflows, AI-driven automation, and secure enterprise-grade architectures is essential.

- A strong data and search foundation is expected, with hands-on experience in PostgreSQL, MongoDB, Elasticsearch/OpenSearch, and vector databases such as Faiss and Pinecone.

- Technical skills should also include Docker, Kubernetes, Git, Terraform or CloudFormation, observability tools, logging and monitoring frameworks, security best practices, and performance optimization, along with demonstrated leadership in guiding teams to deliver and operate production-grade AI solutions.